Road condition monitoring

ABSTRACT

A system for monitoring the condition of a road surface travelled by a plurality of vehicles, each including at least one sensor, is provided. The system includes a central processing arrangement arranged to: map at least a part of the road surface with a number of cells; receive road surface data for the cells, which road surface data is based on measurements made by the sensors as the plurality of vehicles travel on the road surface; and calculate a probability for at least one road surface parameter for each cell travelled by the plurality of vehicles based at least on road surface data received from the plurality of vehicles.

CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/624,959, filed Dec. 20, 2019 now U.S. Pat. No. 10,953,887, issuedMar. 23, 2021, which is a § 371 National Stage Application of PCTInternational Application No. PCT/EP2018/065048 filed Jun. 7, 2018,which claims priority to Swedish Application No. 1750787-2 filed on Jun.20, 2017, each of which are herein incorporated by reference in theirentirety.

TECHNICAL FIELD

The present disclosure relates generally to systems and methods formonitoring the condition of a road surface.

BACKGROUND

Systems that provide vehicles with information about the road conditionsmay be used to increase road safety for conventional vehicles, but willbecome increasingly important with the introduction of autonomouslydriving vehicles. If a vehicle has information about e.g. the frictionon the road, and/or whether there is damage such as potholes in theroad, warnings can be given to the driver/vehicle, and thedriver/vehicle can adapt to the road condition. For example, automaticbraking systems can adapt to the friction on the road.

US20150224925 describes a method for determining a state of a pavement.Data received from at least one sensor that measures a local frictioncoefficient is assigned to individual image sectors of a camera imagecollected by a forward facing camera. Image analysis then provides aclassification of individual pavement segments in camera images todifferent classes, such as e.g. dry, wet, snow-covered or icy, andfriction coefficients are assigned to these segments based on previouslycollected local friction coefficients for image segments belonging tothe same class. The method may use a camera image divided into atwo-dimensional grid in the plane of the pavement, in which casefriction coefficients are assigned to the cells of the grid.

U.S. Pat. No. 9,108,640 describes a method for monitoring and reportingroad quality. Various sensors are used for determining road quality in avehicle, and this data is transmitted, together with the vehicle'slocation, though a mobile network to a central server for distributionin road quality reports. A method for determining an average roadquality indication based on aggregated data from multiple vehicles isalso described. This method may involve calculating a weighted mean,giving more weight to road quality indications coming from trusted andknown sources than those coming from non trusted and unknown sources.

US20120053755 describes a method of calculating the occupancyprobability for a road surface using occupancy grid maps.

PROBLEMS WITH THE PRIOR ART

While US20150224925 addresses the general problem of monitoring thecondition of a road surface, it does not address the issue ofaggregating information from a plurality of vehicles.

While U.S. Pat. No. 9,108,640 addresses the general problem of how tomerge data from different vehicles, it only proposes to calculate meanvalues. This may sometimes give misleading results, especially sincee.g. friction data is only valid a certain period of time afterretrieval, and e.g. potholes may grow over time or quickly disappear dueto mending.

US20120053755 does not address the monitoring of any condition of a roadsurface.

There is thus a need for an improved road condition monitoring system.

SUMMARY

The above described problem is addressed by the claimed system formonitoring the condition of a road surface travelled by a plurality ofvehicles, each comprising at least one sensor. The system comprises acentral processing arrangement arranged to: map at least a part of theroad surface with a number of cells; receive road surface data for thecells, which road surface data is based on measurements made by thesensors as the plurality of vehicles travel on the road surface; andcalculate a probability for at least one road surface parameter for eachcell travelled by the plurality of vehicles based on road surface datareceived from the plurality of vehicles. The central processingarrangement may e.g. be cloud-based, and aggregate road surface datafrom many different sources.

The above described problem is further addressed by the claimed methodfor monitoring the condition of a road surface travelled by a pluralityof vehicles, each comprising at least one sensor. The method comprises:mapping at least a part of the road surface with a number of cells;determining road surface data for the cells based on measurements madeby the sensors as the plurality of vehicles travel on the road surface;transferring the road surface data from the plurality of vehicles to acentral processing arrangement; and calculating a probability for atleast one road surface parameter for each cell travelled by theplurality of vehicles based at least on road surface data received fromthe plurality of vehicles.

This enables an easy merging of many different kinds of road surfacedata, e.g. in the cloud, in order to provide accurate road surfaceparameters.

In embodiments, the central processing arrangement is further arrangedto update the probability based on a road surface parameter specificupdating model that is adapted to the expected development over time ofthe probability for the specific road surface parameter until furtherroad surface data is received for the cell. The road surface parameterspecific updating model may e.g. include the probability increasing ordecreasing over time, e.g. exponentially. This enables accurateestimations of road surface parameters over time even if no new roadsurface data has been received.

In embodiments, the probability of each of a predefined number ofpossible values or value intervals for the at least one road surfaceparameter is calculated, wherein the sum of the probabilities of thesevalues or value intervals for the road surface parameter preferably is 1(100%).

In embodiments, the probability for the at least one road surfaceparameter is calculated in the form of a function, such as e.g. apolynomial function.

In embodiments, the at least one road surface parameter comprises a roadsurface friction parameter p_(f), a road roughness parameter p_(r), apothole parameter p_(h), a speedbump parameter p_(b) and/or an obstacleparameter p_(o).

In embodiments, the at least one sensor comprises a rotational speedsensor for each wheel of each of the plurality of vehicles. A number ofdifferent road surface parameters may be estimated based on signals fromsuch sensors.

In embodiments, each vehicle comprises at least one vehicle processingdevice, and the road surface data for each vehicle is determined in theat least one vehicle processing device based on signals from the atleast one sensor in the vehicle.

In embodiments, the central processing arrangement is arranged tocalculate the probability for the at least one road surface parameterbased also on road surface data received from other sources, such assources of weather data. This enables the updating of road surfaceparameter probabilities even when no vehicles travel on the roadsurface. The weather data may e.g. relate to current and/or expectedtemperature, precipitation, water amount and/or snow amount. The weatherdata may e.g. be processed using a weather model that estimates thefriction as a probability distribution based on the weather data.

In embodiments, the central processing arrangement is arranged toreceive the road surface data via wireless link from the plurality ofvehicles.

The scope of the invention is defined by the claims, which areincorporated into this section by reference. A more completeunderstanding of embodiments of the invention will be afforded to thoseskilled in the art, as well as a realization of additional advantagesthereof, by a consideration of the following detailed description of oneor more embodiments. Reference will be made to the appended sheets ofdrawings that will first be described briefly.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates a system for monitoring the conditionof a road surface, in accordance with one or more embodiments describedherein.

FIG. 2 schematically illustrates a vehicle, in accordance with one ormore embodiments described herein.

FIG. 3 schematically illustrates a road surface mapped with a grid, inaccordance with one or more embodiments described herein.

FIG. 4 schematically illustrates a road surface mapped with segments, inaccordance with one or more embodiments described herein.

FIGS. 5a and 5b schematically illustrate road surface parameterprobability updating models, in accordance with one or more embodimentsdescribed herein.

FIG. 6 schematically illustrates a method for monitoring the conditionof a road surface, in accordance with one or more embodiments describedherein.

Embodiments of the present disclosure and their advantages are bestunderstood by referring to the detailed description that follows. Itshould be appreciated that like reference numerals are used to identifylike elements illustrated in one or more of the figures.

DETAILED DESCRIPTION

The present disclosure relates generally to systems and methods formonitoring the condition of a road surface. Embodiments of the disclosedsolution are presented in more detail in connection with the figures.

FIG. 1 schematically illustrates a system 100 for monitoring thecondition of a road surface travelled by a plurality of vehicles 200, inaccordance with one or more embodiments described herein. The system 100comprises a central processing arrangement 120, which may e.g. be cloudbased. The vehicle 200 shown schematically in FIG. 1 comprises at leastone sensor 130, a vehicle processing device 110 and a GPS receiver 140.

FIG. 2 schematically illustrates a vehicle 200, in accordance with oneor more embodiments described herein. The vehicle 200 shown in FIG. 2comprises a vehicle processing device 110 and four sensors 130.

The sensors may e.g. be rotational speed sensors 130, one for eachwheel. The sensors 130 may also be other types of sensors. Fordetermining load changes in the vehicle, information about e.g. thetorque and the engine RPM may be needed, and this can e.g. be determinedbased on measurements from various engine sensors. A pressure sensorsuch as e.g. a barometer may be used to determine the altitude, andbased on this determine whether the vehicle is driving uphill ordownhill. Various types of accelerometers and/or gyroscopes, e.g. in theform of an inertial measurement unit (IMU), may be used to determinee.g. yaw rate, longitudinal acceleration and vertical acceleration, tobe used for determine load changes in the vehicle. Axle heightinformation may e.g. be provided by sensors which are arranged in thevehicle for adapting the direction of the vehicle lights. The absolutevelocity may e.g. be determined using a GPS sensor, or using anothervelocity recognizing sensor such as a camera, a radar, an IR sensor, ora laser sensor. Information from a temperature sensor may assist indetermining friction.

The signals from the sensors 130 may be transferred, e.g. wirelessly,either directly from the sensors 130 to the central processingarrangement 120, or via the vehicle processing device 110. Road surfacedata may be determined based on the signals from the at least one sensor130, either in the vehicle processing device 110 or in the centralprocessing arrangement 120. The central processing arrangement 120 maye.g. be cloud-based, and aggregate road surface data from many differentsources.

The central processing arrangement 120 may be arranged to map the roadsurface 300 with a number of cells 320, which may e.g. be cells 320forming an array or grid 310, as shown in FIG. 3, or cells 320 in theform of segments of the road, as shown in FIG. 4. The sensor 130, whichis arranged in the vehicle 200, may make measurements as the vehicle 200travels on the road surface 300. Signals from the sensor 130, or roadsurface data determined based on them, may be transmitted from thevehicle 200 to the central processing arrangement 120 via wireless link,e.g. using the vehicle processing device 110. The central processingarrangement 120 may then calculate the probability for at least one roadsurface parameter for each cell 320 based on road surface data receivedfrom a plurality of vehicles 200.

The cells 320 may have any size or shape. If many vehicles traveldifferent parts of the road so that the central processing arrangement120 receives a lot of road surface data, and the exact location of thesevehicles is known (from e.g. GPS data), a small cell size as shown inFIG. 3 may be desirable, since this provides a high resolution of thedata, which is useful for e.g. locating potholes. However, for lesstravelled road stretches, where there will not be road surface datacollected for all parts of the road surface 300, it may be better to usecells 320 in the form of larger road segments, so that there will atleast be relevant road friction data for the segment as a whole. Suchsegments may have any size, such as 1 m long, 10 m long or even 100 mlong. If the road has several lanes in each direction, each lane mayhave its own segment, since e.g. the friction may be different in lessertravelled lanes than in more travelled lanes. It is however alsopossible to consider the whole road, along a certain length such as e.g.50 m, to be a cell 320.

Each cell 320 may be assigned a probability for each of a number ofdifferent road surface parameters. The road surface parameters for eachcell 320 may e.g. be a road surface friction parameter p_(f), a roadroughness parameter p_(r), a pothole parameter p_(h), a speedbumpparameter p_(b), and/or an obstacle parameter p_(o). Other parameterscould also be used, such as e.g. a parameter describing the amount ofwater on the road. Each of these parameters may be assigned a number ofdifferent values, which may be calculated based on road surface datadetermined based on signals from the sensors 130. The probabilities foreach of these parameters may alternatively be expressed as a valueinterval or as a function, e.g. a polynomial, instead of as a number ofdiscrete values.

The road surface friction parameter p_(f) may e.g. be assigned a valuefrom 1 to 3, where 3 defines “high friction” and 1 defines “lowfriction”. The road surface friction parameter p_(f) should thus beassigned 1 if the friction is high (good grip), 2 if the friction ismedium (e.g. gravel or snow), and 3 if the friction is low (e.g. ice).It is of course possible to classify the friction in other, moredetailed ways, using many more friction classes, or even using theactual friction coefficients calculated in the vehicles 200, e.g. in theElectronic Stability Control (ESC) system. However, for purposes likealerting the driver/vehicle, adapting the vehicle to the road conditionor proposing the best route in a navigating system, the informationgiven by a classification of the friction into a relatively low numberof classes, such as e.g. three, four or five classes, will be enough.The actual friction coefficients determined in each vehicle 200 maystill be reported to the central processing arrangement 120 and used fordetermining the probabilities of different values for the road surfacefriction parameter p_(f).

One way of using actual friction coefficients is to represent thefriction potential as a histogram with a number of bins, eachrepresenting a certain interval for the friction coefficient. Thefriction coefficient may e.g. have a value between 0 and 1.2, and ifthis is represented as a histogram with 24 bins, the first bin willrepresent a friction coefficient of 0-0.05, the second bin willrepresent a friction coefficient of 0.05-0.10, etc. The centralprocessing arrangement 120 may in this case be arranged to sort theactual friction coefficients calculated in each vehicle 200 into thecorrect bin, and thereby base the determining of the probabilities offriction within the different intervals for the road surface frictionparameter p_(f) on the actual friction coefficients reported from thevehicles 200.

The road roughness parameter p_(r) may e.g. be assigned a value from 1to 6, where 1 defines a “very smooth road” and 6 defines a “very roughroad”. It is of course possible to classify the road roughness in other,more detailed ways, e.g. using the International Roughness Index (IRI).However, for purposes like alerting the driver/vehicle, adapting thevehicle to the road condition or proposing the best route in anavigating system, the information given by a classification of the roadroughness into a relatively low number of classes, such as e.g. three,four, five or six classes, will be enough. The actual road roughnessdetermined in each vehicle 200 may still be reported to the centralprocessing arrangement 120 and used for determining the probabilities ofdifferent values for the road roughness parameter p_(r). This may e.g.be done in the way described above for the road surface frictionparameter p_(f).

The pothole parameter p_(h) may e.g. be assigned a value of 0 (“nopothole present in the cell”) or 1 (“pothole present in the cell”). Itis possible to instead use a pothole scale of e.g. 1 to 4, where 1defines “no pothole present in the cell”, 2 defines “small potholepresent in the cell”, 3 defines “regular pothole present in the cell”,and 4 defines “large pothole present in the cell”. It is possible toinstead use more specific values of e.g. 1 (“pothole right side”), 2(“pothole left side”), and 3 (“pothole both sides”).

The speedbump parameter p_(b) may e.g. be assigned a value of 0 (“nospeedbump present in the cell”) or 1 (“speedbump present in the cell”).

The obstacle parameter p_(o) may e.g. be assigned a value of 0 (“noobstacle present in the cell”) or 1 (“obstacle present in the cell”). Itis possible to instead use an obstacle scale of e.g. 1 to 3, where 1defines “no obstacle present in the cell”, 2 defines “small obstaclepresent in the cell”, and 3 defines “large obstacle present in thecell”. The obstacles defined by the obstacle parameter p_(o) shouldpreferably be temporary obstacles, such as e.g. pieces of wood, rocks orother debris on the road. For each cell 320, different values for theroad surface parameters may have different probabilities. A specificcell 320 may e.g. have a probability β_(b0) of 0.8 (80%) of thespeedbump parameter p_(b) being 0, but at the same time a probabilityβ_(b1) of 0.2 (20%) of the speedbump parameter p_(b) being 1. Theprobabilities may be based on road surface data received both from thevehicles 200 travelling on the road surface 300 and from other sources,such as e.g. sources of weather data or information from a roadauthority. The different items of road surface data received for eachcell from the various sources may be aggregated by being weightedtogether into different probabilities of different values for each ofthe different road surface parameters. The weighting may e.g. be basedon the age of the road surface data, so that new road surface data isweighted higher than old road surface data.

The weighting may also, or alternatively, be based e.g. on the generalreliability of the source of the road surface data. Some sources of roadsurface data may be considered to be more reliable than other sources ofroad surface data, based e.g. on the types of sensors used to collectthe road surface data. The reliability of a source of road surface datamay also be determined automatically by the central processingarrangement 120 based e.g. on the correspondence of previous roadsurface data received from the source with other sources of road surfacedata. A vehicle 200 which consistently delivers road surface data thatdeviates from road surface data received from other vehicles 200travelling on the same road surface 300 and delivering road surface datafor the same cells 320 may e.g. be assigned a general low reliabilityand thus be weighted lower.

If road surface data is received from a road authority, it shouldpreferably be weighted very high. The road authority may e.g. turn apart of a gravel road into an asphalt road, and this may affect both theroad roughness and the road friction. The road surface authority mayalso e.g. arrange or remove speedbumps, and this of course affects thespeedbump parameter. If the road authority reports that a speedbump hasbeen arranged in a certain position, the probability β_(b1) of thespeedbump parameter p_(b) being 1 may e.g. automatically be set to 1(100%) for the affected cells 320, regardless of any road surface datareceived from vehicles 200. In the same way, if the road authorityreports that a pothole in a certain position has been repaired, theprobability β_(h0) of the pothole parameter p_(h) being 0 mayautomatically be set to 1 and the probability β_(h1) of the potholeparameter p_(h) being 1 may automatically be set to 0 for the affectedcells 320, regardless of any road surface data received from vehicles.The probability β_(h1) may then of course rise over time, since thepothole may come back after repair.

The different road surface parameters may also be correlated, so that acertain probability for one road surface parameter affects theprobability for other road surface parameters. If there e.g. is a highprobability of a pothole or a speedbump being present in a cell 320,this may affect the validity of the determined friction coefficients,since potholes and speedbumps may cause the wheels to slip, and thusaffect the determined friction coefficients. The probabilities β_(fx) ofthe different values for the road surface friction parameter p_(f) maythus e.g. be determined based also e.g. on the probabilities β_(hx) andβ_(bx) of the different values for the pothole parameter p_(h) and thespeedbump parameter p_(b).

If no vehicles have passed a particular cell 320, and no other roadsurface data has been received from other sources for the cell 320,there are a number of different ways of assigning probabilities. It maye.g. be decided that since no information is available, the probabilityof each value for each parameter is equal. If there are e.g. threeclasses for the road surface friction parameter p_(f), each of them maybe assigned a probability β_(fx) of 0.33 (33%), since they are allequally likely if there is no information available. However, for someparameters, the likelihood of a certain value is inherently higher thanthe likelihood of another value. It is e.g. much more likely that thereis no speedbump present in a cell 320 than that there is a speedbumppresent in a cell 320, since most road stretches do not have speedbumps.It is therefore possible to assign the probabilities of different valuesfor the different parameters based on statistical expectations forsimilar types of cells 320. The statistical expectation may e.g. be ahigh probability of the road surface friction parameter p_(f) and theroad roughness parameter p_(r) having average values, and of the potholeparameter p_(h) and the speedbump parameter p_(b) having values definingno potholes and no speedbumps. Another way of assigning probabilities ofthe different values for the road surface parameters is to base them onthe values of neighboring cells. However, as soon as any “real” roadsurface data has been received for the particular cell 320, the assignedprobabilities should preferably be exchanged for new probabilities,calculated based on the received road surface data.

If road surface data is received at a high enough frequency, thecalculated probabilities of the different values for the various roadsurface parameters of each cell 320 can be expected to be reasonablyaccurate. However, for less travelled road stretches, it may beadvantageous if the calculated probabilities of the cell 320 are updatedeven if no new road surface data has been received for a cell 320. Suchupdating is preferably effected using an updating model that is specificfor the road surface parameter, in order to provide a relevantprediction of what can be expected to happen between vehicleobservations. Such a road surface parameter specific updating model ispreferably adapted to the expected development over time of theprobability for the specific road surface parameter. The updating modelmay e.g. be continuously improved by comparing predicted values withreceived road surface data the next time road surface data is received.The updating model may e.g. be based on the general model of a dynamicsystem:x _(k+1) =f _(k)(x _(k) ,u _(k) ,w _(k),θ_(k))y _(k) =h _(k)(x _(k) ,u _(k) ,e _(k),θ_(k))The probabilities β_(fx) of different values for the road surfacefriction parameter p_(f) may e.g. be updated using a friction updatingmodel that is preferably adapted to the expected development over timeof the probabilities for the road surface friction parameter p_(f). Theroad surface friction may e.g. change due to weather conditions. Currentand expected weather conditions may be reported into the system 100 byvarious weather data sources, and the probabilities β_(fx) of the roadsurface friction parameter p_(f) having certain values may change basedon these reported current and expected weather conditions according to apredefined friction updating model. If e.g. a certain cell 320 has aprobability β_(f1) of 0.9 (90%) of the road surface friction parameterp_(f) being 1 (“low friction”) at a point in time where the temperatureis 0° C., and the temperature is expected to rise to 10° C. within a fewhours, the friction can be expected to become higher over time. Thefriction updating model for such a situation may e.g. be linear, if thetemperature is expected to rise approximately linearly. The probabilityβ_(f1) of the road surface friction parameter p_(f) being 1 will thus bedecreased linearly, and the probability β_(f2) of the road surfacefriction parameter p_(f) being 2 will be increased correspondingly.However, if actual measured road surface friction data is received, thisshould preferably be used instead of the calculated estimates. In suchcases, the friction updating model may be updated based on thedifference between the calculated values and the actual measured roadsurface friction data.

The probabilities β_(hx) of different values for the pothole parameterp_(h) may e.g. be updated using a pothole updating model that ispreferably adapted to the expected development over time of theprobabilities for the pothole parameter p_(h). If the probability β_(h1)of the pothole parameter p_(h) being 1 for a cell 320 is e.g. set to 0.9based on received road surface data, and no further road surface data isreceived for the cell, there may be a chance that the pothole has beenrepaired without this being reported from any road surface authority. Insuch a case, a pothole updating model using a forgetting factor shouldpreferably be used, so that the probability β_(h1) of the potholeparameter p_(h) being 1 is decreased over time and the probabilityβ_(h0) of the pothole parameter p_(h) being 0 is correspondinglyincreased. The pothole updating model may e.g. involve a relatively fastexponential decreasing of the probability β₁, as shown in FIG. 5 a.

The probabilities β_(bx) of different values for the speedbump parameterp_(b) may e.g. be updated using a speedbump updating model that ispreferably adapted to the expected development over time of theprobabilities for the speedbump parameter p_(b). If the probabilityβ_(b1) of the speedbump parameter p_(b) being 1 for a cell 320 is e.g.set to 0.9 based on received road surface data, and no further roadsurface data is received for the cell, there may be a chance that thespeedbump has been removed without this being reported from any roadsurface authority. In such a case, a speedbump updating model using aforgetting factor should preferably be used, so that the probabilityβ_(b1) of the speedbump parameter p_(b) being 1 is decreased over timeand the probability β_(b0) of the speedbump parameter p_(b) being 0 iscorrespondingly increased. The speedbump updating model may e.g. involvea relatively slow exponential decreasing of the probability β_(b1), asshown in FIG. 5 b.

Since road roughness may also change over time, the probabilities β_(rx)of different values for the road roughness parameter p_(r) may e.g. beupdated using a road roughness updating model that is preferably adaptedto the expected development over time of the probabilities for the roadroughness parameter p_(r). The road roughness updating model may e.g.involve a relatively slow exponential change of the probabilities β_(rx)of different values for the road roughness parameter p_(r).

Since the obstacles defined by the obstacle parameter p_(o) arepreferably temporary obstacles, such as e.g. pieces of wood, rocks ordebris on the road, the probabilities β_(ox) of different values for theobstacle parameter p_(o) may change quickly. It is quite likely that atemporary obstacle has been removed when a certain time has passed, andthus the obstacle updating model may e.g. involve a relatively fastexponential decreasing of the probability β_(o1) of the obstacleparameter p_(o) being 1 in order to be adapted to the expecteddevelopment over time of the probabilities for the obstacle parameterp_(o).

The above approach is now illustrated by a simple example for onespecific cell 320, using the general modelx _(k) =λx _(k−1)y _(n) =x+e _(n)where the first equation is a time-update, used to update the statex_(k) between up-linked values from the vehicles to the centralprocessing arrangement 120. Taking the pothole identification as anexample, during initialization x₀ is set to the initial probabilityβ_(hx) for the exemplified pothole parameter p_(h). The parameter λcontrols the forgetting of this initial probability β_(hx) over timewhen no measurement is obtained from any vehicle. However, if theremoval of the pothole is reported from a road surface authority, thiswill instantaneously set x_(k) to zero, indicating that the probabilityis zero for a pothole to be found in this cell 320.

Measurements y_(n) from a plurality of vehicles 200 are used to estimatethe unknown probability x for the presence of a pothole in the cell 320.Here we assume an additive model, where e_(n) is measurement noisecaused by for example GPS position inaccuracies. Assuming that y_(n) cantake the values pothole detected (1) or pothole not detected (0), theestimate of the probability x may be given by the measurement updatex _(k) =x _(k−1)+(y _(n) −x _(k−1))/nwhich is basically a recursive calculation of the average value for nmeasurements. Hence, the time-update is calculated periodically in thecentral processing arrangement 120 over time, but this measurementupdate is calculated only when a measurement is received.

This scheme may be improved by indicating so called hotspots on the map.For example, if one vehicle has detected that a pothole is present in aparticular cell 320, this cell 320 is then set to be a hotspot. Vehiclesentering this cell/hotspot 320 will then be forced to report if apothole is detected or not.

Other approaches also available are e.g. occupancy grid mapping andother statistical methods with similar properties.

The system 100 may thus comprise a central processing arrangement 120and sensors 130 arranged in each of the plurality of vehicles 200. Thecentral processing arrangement 120 may be arranged to: map at least apart of the road surface 300 with a number of cells 320; receive roadsurface data for the cells 320, which road surface data has beencollected by the sensors 130 as the plurality of vehicles 200 travel onthe road surface 300; and calculate the probability for at least oneroad surface parameter for each cell 320 travelled by the plurality ofvehicles 200 based at least on road surface data received from theplurality of vehicles 200.

The central processing arrangement 120 may further be arranged to updatethis probability based on a road surface parameter specific updatingmodel that is preferably adapted to the expected development over timeof the probability for the specific road surface parameter until furtherroad surface data is received for the cell 320. The road surfaceparameter specific updating model may e.g. include the probabilityincreasing or decreasing exponentially over time. This enables accurateestimations of road surface parameters over time even if no new roadsurface data has been received.

The at least one sensor 130 may e.g. provide information about

-   -   a pressure of the tire    -   a temperature of the tire    -   an ambient temperature    -   an axle height    -   a suspension pressure    -   a suspension height    -   a tire type, manually entered via a human-machine-interface    -   an estimated friction potential from an ABS braking    -   an estimated friction potential from an TCS event    -   a normalized traction force on the wheel    -   a friction related value    -   a torque applied on the wheel    -   a longitudinal acceleration    -   a lateral acceleration    -   a vertical acceleration    -   a brake pressure    -   a yaw rate    -   a vehicle speed    -   a wheel speed    -   a steering wheel angle    -   a wheel angle    -   a wiper speed    -   an ambient humidity    -   values derived from any type of image (optical, infrared, etc.)        such as road surface temperature, sky cloudiness, etc.    -   values derived from any type of external sensor such as radar,        laser etc.    -   a control flag register.

Examples of flags of the control flag register may e.g. includeindications of whether ESC control is in progress, ABS braking is inprogress, TCS is in progress, braking is in progress, a gear shift is inprogress, the clutch pedal is engaged, a reverse gear is engaged, atrailer is connected, or a cruise control is engaged.

FIG. 6 schematically illustrates a method 600 for monitoring thecondition of a road surface 300 travelled by a plurality of vehicles200, each comprising at least one sensor 130. The method comprises:

Step 610: mapping the road surface 300 with a number of cells 320.

Step 620: determining road surface data for the cells 320 based onmeasurements made by the plurality of sensors 130 as the plurality ofvehicles 200 travel on the road surface 300.

Step 630: transferring the road surface data from the plurality ofvehicles 200 to a central processing arrangement 120.

Step 640: calculating the probability for at least one road surfaceparameter for each cell 320 travelled by the plurality of vehicles 200based on road surface data received from the plurality of vehicles 200.

This enables an easy merging of many different kinds of road surfacedata, e.g. in the cloud, in order to provide accurate road surfaceparameters.

In embodiments, the method 600 may further comprise:

Step 650: updating this probability based on a road surface parameterspecific updating model that is adapted to the expected development overtime of the probability for the specific road surface parameter untilfurther road surface data is received for the cell 320.

The road surface parameter specific updating model may e.g. include theprobability increasing or decreasing exponentially over time.

The probability of each of a predefined number of possible values orvalue intervals for the at least one road surface parameter may becalculated. The sum of the probabilities of these values or valueintervals for the road surface parameter should preferably be 1 (100%).It is however not necessary that the sum of the probabilities of thesevalues or value intervals for the road surface parameter is 1.

The probability for the at least one road surface parameter may also becalculated in the form of a function, such as e.g. a polynomialfunction.

The at least one road surface parameter may e.g. comprise a road surfacefriction parameter p_(f), a road roughness parameter p_(r), a potholeparameter p_(h), a speedbump parameter p_(b), and/or an obstacleparameter p_(o).

The at least one sensor may e.g. comprise a rotational speed sensor 130for each wheel of each of the plurality of vehicles 200.

The determining 620 of the road surface data for each vehicle may e.g.take place in at least one vehicle processing device 110 comprised inthe vehicle 200 based on signals from the at least one sensor 130 in thevehicle 200.

The calculating 640 of the probability for the value for the at leastone road surface parameter may e.g. be based also on road surface datareceived from other sources, such as sources of weather data. Theweather data may e.g. relate to current and/or expected temperature,precipitation, water amount and/or snow amount. The weather data maye.g. be processed using a weather model that estimates the friction as aprobability distribution based on the weather data.

The transferring 630 of the road surface data from the plurality ofvehicles 200 to the central processing arrangement 120 may e.g. takeplace via wireless link.

The foregoing disclosure is not intended to limit the present inventionto the precise forms or particular fields of use disclosed. It iscontemplated that various alternate embodiments and/or modifications tothe present invention, whether explicitly described or implied herein,are possible in light of the disclosure. Accordingly, the scope of theinvention is defined only by the claims.

The invention claimed is:
 1. System for monitoring the condition of aroad surface travelled by a plurality of vehicles, each comprising atleast one sensor, the system comprising a central processing arrangementarranged to: map at least a part of the road surface with a number ofcells; receive road surface data for the cells, which road surface datais based on measurements made by the sensors as the plurality ofvehicles travel on the road surface; and calculate a probability of avalue for at least one road surface parameter for each cell travelled bythe plurality of vehicles based at least on road surface data receivedfrom the plurality of vehicles, wherein the central processingarrangement is arranged to calculate said probability by aggregatingitems of road surface data received from at least two different vehiclesby weighting them together into different probabilities of differentvalues for each at least one road surface parameter.
 2. System accordingto claim 1, wherein the at least one road surface parameter comprises aroad roughness parameter (p_(r)), a pothole parameter (p_(h)), aspeedbump parameter (p_(b)) and/or an obstacle parameter (p_(o)). 3.System according to claim 1, wherein the central processing arrangementis arranged to base the weighting on the age of the road surface data,so that new road surface data is weighted higher than old road surfacedata.
 4. System according to claim 1, wherein the central processingarrangement is arranged to base the weighting on a reliability score foreach vehicle, where central processing arrangement is arranged todetermine the value of the reliability score based on a comparison ofprevious road surface data received from the vehicle with previous roadsurface data received from other vehicles, so that road surface datafrom a vehicle having a low reliability score is weighted lower thanroad surface data from a vehicle having a high reliability score. 5.System according to claim 1, wherein the central processing arrangementis arranged to calculate the probability of the value for the at leastone road surface parameter in the form of a function, e.g. a polynomialfunction.
 6. System according to claim 1, wherein the central processingarrangement is arranged to update the probability based on a roadsurface parameter specific updating model that is adapted to theexpected development over time of the probability for the specific roadsurface parameter until further road surface data is received for thecell, wherein the road surface parameter specific updating modelincludes the probability increasing or decreasing exponentially overtime.
 7. System according to claim 1, wherein the central processingarrangement is arranged to calculate the probability of each of apredefined number of possible values or value intervals for the at leastone road surface parameter, wherein the sum of the probabilities ofthese values or value intervals for the road surface parameterpreferably is 1 (100%).
 8. System according to claim 1, wherein the atleast one sensor comprises a rotational speed sensor for each wheel ofeach of the plurality of vehicles.
 9. System according to claim 1,wherein each vehicle comprises at least one vehicle processing device,and the road surface data for each vehicle is determined in the at leastone vehicle processing device of the vehicle based on signals from theat least one sensor in the vehicle.
 10. System according to claim 1,wherein the central processing arrangement is arranged to calculate theprobability for the at least one road surface parameter based also onroad surface data received from other sources, such as sources ofweather data.
 11. Method for monitoring the condition of a road surfacetravelled by a plurality of vehicles, each comprising at least onesensor, the method comprising: mapping at least a part of the roadsurface with a number of cells; determining road surface data for thecells based on measurements made by the sensors as the plurality ofvehicles travel on the road surface; transferring the road surface datafrom the plurality of vehicles to a central processing arrangement; andcalculating a probability of a value for at least one road surfaceparameter for each cell travelled by the plurality of vehicles based atleast on road surface data received from the plurality of vehicles,wherein said probability is calculated by aggregating items of roadsurface data received from at least two different vehicles by weightingthem together into different probabilities of different values for eachat least one road surface parameter.
 12. Method according to claim 11,wherein the at least one road surface parameter comprises a roadroughness parameter (p_(r)), a pothole parameter (p_(h)), a speedbumpparameter (p_(b)), and/or an obstacle parameter (p_(o)).
 13. Methodaccording to claim 11, further comprising basing the weighting on theage of the road surface data, so that new road surface data is weightedhigher than old road surface data.
 14. Method according to claim 11,further comprising basing the weighting on a reliability score for eachvehicle, where the reliability score is determined based on a comparisonof previous road surface data received from the vehicle with previousroad surface data received from other vehicles, so that road surfacedata from a vehicle having a low reliability score is weighted lowerthan road surface data from a vehicle having a high reliability score.15. Method according to claim 11, wherein the probability of the valuefor the at least one road surface parameter is calculated in the form ofa function, such as e.g. a polynomial function.
 16. Method according toclaim 11, further comprising updating the probability based on a roadsurface parameter specific updating model that is adapted to theexpected development over time of the probability for the specific roadsurface parameter until further road surface data is received for thecell, wherein the road surface parameter specific updating modelincludes the probability increasing or decreasing exponentially overtime.
 17. Method according to claim 11, wherein the probability of eachof a predefined number of possible values or value intervals for the atleast one road surface parameter is calculated, wherein the sum of theprobabilities of these values or value intervals for the road surfaceparameter preferably is 1 (100%).
 18. Method according to claim 11,wherein the at least one sensor comprises a rotational speed sensor foreach wheel of each of the plurality of vehicles.
 19. Method according toclaim 11, further comprising determining the road surface data for eachvehicle in at least one vehicle processing device comprised in thevehicle based on signals from the at least one sensor of the vehicle.20. Method according to claim 11, further comprising calculating theprobability for the at least one road surface parameter based also onroad surface data received from other sources, such as sources ofweather data.